Methods and apparatus to monitor digital media

ABSTRACT

Methods, apparatus, systems and articles of manufacture to monitor media are disclosed. An example apparatus includes an orderer to order metadata according to timing information, the metadata corresponding to digital media from a media provider; a data shifter to adjust the ordered metadata using a plurality of offsets to generate a plurality of adjusted ordered metadata; a comparator to perform a plurality of comparisons by comparing the plurality of adjusted ordered metadata to reference data; and compute a plurality of errors corresponding to the plurality of comparisons; and the data shifter to identify an offset of the plurality of offsets corresponding to a lowest computed error of the plurality of errors; and adjust the ordered metadata using the offset.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 16/234,528, (Now U.S. Pat. No. 10,880,600) which was filed on Dec.27, 2018. U.S. patent application Ser. No. 16/234,528 is herebyincorporated herein by reference in its entirety. Priority to U.S.patent application Ser. No. 16/234,528 is hereby claimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to media monitoring and, moreparticularly, to methods and apparatus to monitor digital media.

BACKGROUND

Media players on electronic devices (e.g., smartphones, tabletcomputers, computers, smart televisions, set top boxes, etc.) enableaccess to a wide range of digital media (e.g., streamed media or mediaon-demand). The media can be streamed from the Internet via a browser oran application dedicated for streaming media or playing media.Additionally or alternatively, a media provider (e.g., CBS, HBO, NBC,etc.) may provide media on-demand to allow a user to watch movies,television programs, etc., as part of their subscription to the mediaprovider. Many media streaming websites, media streaming applications,or media on-demand include and/or are required to provide advertisementsalong with content selected for presentation by a viewer or machine(e.g., web crawler).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example environment for monitoring media inconjunction with teachings of this disclosure.

FIG. 2 is a block diagram of an example implementation of the mediaanalyzer of FIG. 1.

FIGS. 3 and 4 are flowcharts representative of example machine readableinstructions which may be executed to implement the example mediaanalyzer of FIGS. 1 and/or 2.

FIG. 5 is a block diagram of an example processor platform structured toexecute the instructions of FIGS. 3 and/or 4 to implement the examplemedia analyzer of FIGS. 1 and/or 2.

The figures are not to scale. In general, the same reference numberswill be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Consuming media presentations generally involves listening to audioinformation and/or viewing video information such as, for example, radioprograms, music, television programs, movies, still images, etc.Media-centric companies such as, for example, advertising companies,broadcast networks, etc. are often interested in verifying that a mediaprovider is outputting advertisements according to an agreement betweenthe media centric companies and the media providers. A technique thatmay be used to measure the exposure to media involves awarding mediaexposure credit to a media presentation for each audience member that isexposed to the media presentation.

Media exposure credit is often measured by monitoring the mediaconsumption of audience members using, for example, metering devices. Ameter may be an electronic device and/or may be software implemented inan electronic device configured to monitor media consumption (e.g.,viewing and/or listening activities) using any of a variety of mediamonitoring techniques. For example, when a media provider (e.g., CBS,HBO, NBC, etc.) enters into an agreement with an audience measuremententity (AME) (e.g., The Nielsen Company (U.S.), LLC), the media providermay agree to insert ancillary codes (e.g., watermarks) and/or ID3 tagsinto the media prior to distribution of the media to media outputdevices (e.g., smartphones, set-top-boxes (STBs), computers, smarttelevisions, and/or any other client device that is capable ofoutputting media). The meter may be installed on a media output deviceof the user or panelist. The meter searches for the watermarks and/orID3 tags and transmits the obtained watermarks and/or ID3 tags to theAME. Panelists are persons that have agreed to be monitored by, forexample, an audience measurement entity (AME) such as The NielsenCompany (U.S.), LLC. Typically, such panelists provide detaileddemographic information (e.g., race, age, income, home location,education level, gender, etc.) when they register to participate in thepanel and agree to have the meter installed on one or more media outputdevice.

In examples illustrated below, media exposure metrics are monitored byretrieving metadata embedded in or otherwise transported with the mediapresented via a media presenter of the client device (e.g., based on theagreement between the media provider and the AME). In some examples, themetadata is stored in a Document Object Model (DOM) object. In someexamples, the metadata is stored in an ID3 tag format, although anyother past, present, and/or future metadata format may additionally oralternatively used. The DOM is a cross-platform and language-independentconvention for representing and interacting with objects in HypertextMarkup Language (HTML).

In some examples, media presenters (e.g., media plugins) such as, forexample, the QuickTime player, emit DOM events that can be captured viaJavaScript. By capturing the DOM events triggered by the mediapresenter, it is possible to extract the metadata (e.g., the ID3 tagdata) via the DOM. The metadata may be combined with other informationsuch as, for example, cookie data associated with the user of thedevice, and transmitted to, for example, a central facility for analysisand/or computation with data collected from other devices. Thecollection of ID3 tags from a plurality of media output devicescorresponding to digital media (e.g., streaming media, media on demand,etc.) is herein referred to as census data.

Example methods, apparatus, systems, and articles of manufacturedisclosed herein involve collecting census data to extract metadata(e.g., metadata stored in an ID3 tag, extensible markup language (XML)based metadata, and/or metadata in any other past, present, and/orfuture format) associated with digital media transmissions (e.g.,streaming audio, streaming video, video on-demand, and/or audio ondemand) at a client device to perform validation procedure(s).Validation procedure(s) may include determining that a media provider isinserting advertisements in digital media according to an agreement(e.g., the media provider is outputting the agreed upon agreements atthe agreed upon times, the media provider is not inserting additionaladvertisements in the media, the media provider is not dynamicallyinserting ads during national commercials, etc.) and/or identifyingtranscoder outages.

In some examples, a media provider may provide the same advertisementsfor online media as it does for the broadcast media. In such examples,the AME can credit online media in a particular category based on theadvertisements in the broadcast media. Part of an agreement between amedia provider and an AME may correspond to specific durations of time.For example, an agreement between an AME and a media provider mayrequire that the media provider play advertisements during predefinedcommercial pods (e.g., duration of time dedicated only foradvertisements). However, when ID3 tags are collected by meter, thetiming information stored in the ID3 tag and/or the metadata of the ID3tag may be inaccurate when the timer used by the meter and/or the deviceimplementing the meter may be inaccurate. For example, if the timer is10 seconds slow, the census data corresponding to the timer will be 10seconds off, thereby affecting the validation of the agreement.Accordingly, examples disclosed herein determine and apply an offset tothe census data to align the census data with linear reference data.Once aligned, the offset census data can be accurately processed forvalidation.

Additionally, examples disclosed herein reduce the amount of resourcesneeded to perform validation based on census data by first extractingraw ID3 tags from the census data, as opposed to decrypting the entireID3 tag. Extracting raw ID3 tags consumes less resources than decryptingID3 tags. Once the raw ID3 tag is extracted, examples disclosed hereinidentify and discard duplicate ID3 tags prior to decryption. In thismanner, the number of ID3 tags to decrypt is much less than the totalnumber of ID3 tags including duplicates, thereby consuming lessresources than processing all ID3 tags of obtained census data. Usingexamples disclosed herein, census data may be used in a validationprocess in minutes instead of hours.

FIG. 1 illustrates an example environment 100 for monitoring digitalmedia in conjunction with teachings of this disclosure. The exampleenvironment 100 includes an example media provider 102, an example user104, an example media device 106, an example AME application 108, anexample wireless interface 110, an example network 112, an example AME114, an example media analyzer 116, and an example database 118.

The media provider 102 of the illustrated example of FIG. 1 correspondsto any one or more media provider(s) capable of providing media forpresentation via the media device 106. The media provided by the mediaprovider 102 can provide any type(s) of media, such as audio, video,multimedia, etc. Additionally, the media can correspond to live media,streaming media, broadcast media, stored media, on-demand content, etc.In some examples, the media provider 102 of the illustrated example ofFIG. 1 is a server providing Internet media (e.g., web pages, audio,videos, images, etc.). The media provider 102 may be implemented by abroadcast provider (cable television service, fiber-optic televisionservice, etc.) and/or a digital media provider (e.g., Internetstreaming/on-demand video such as Netflix®, YouTube®, Hulu®, HBO GO,etc. and/or audio services such as Spotify®, Shoutcast®, Stitcher®,Pandora®, Last.fm®, etc.) and/or any other provider of streaming mediaservices and/or media on-demand services. In some other examples, themedia provider 102 is a host for web site(s). Additionally oralternatively, the media provider(s) 102 may not be on the Internet. Forexample, the media provider may be on a private and/or semi-privatenetwork (e.g., a LAN, a virtual private network) to which the mediadevice 106 connects via the example network 112. The media provider 102may include a transcoder to extract AME watermarks and generate an ID3tag including a plurality of AME watermarks. For example, the AME 114may insert a watermark into audio streams included in media (e.g., whichmay or may not be timestamped). The transcoder of the media provider 102receives the audio stream, reads, decodes, and interprets the encodedaudio to extract and analyze the AME watermarks (e.g., to determinewatermark types). Once analyzed, the transcoder generates an ID3 tagwith a plurality of watermarks. Before transmitting the ID3 tag, thetranscoder timestamps the start time (e.g., relative or absolute), whenthe transcoder began to process the audio for the ID3 tag and the endtime (e.g., relative or absolute), when the transcoder stoppedprocessing the audio for the ID3 tag. Additionally, the exampletranscoder adds an offset value for each watermark. The offset valuecorresponds to the amount of time after the start time that thetranscoder detected the watermark. Once timestamped, the transcoderinserts the ID3 tag into media (e.g., a MPEG stream segment) andtransmits the media to the example media device 106 via the network 112.

The user 104 of FIG. 1 may be a person that has agreed to be monitoredby the AME 114 (e.g., a panelist). When the user 104 agrees to be partof a panel, the user 104 is required to install and/or have installedthe example AME application 108 on the media device 106 to be able totransmit census data gathered on the media device 106 to the AME 114 viathe media device 106, as further described below. In other examples, theuser 104 is not a panelist. In such examples, the AME 114 may have anagreement with the media provider 102, where the media provider 102allows the AME 114 to install a software development kit (SDK) tomonitor the media displayed on the media device 106 (e.g., the SDKimplementing the example AME application 108) and transmit obtained IDtags to the AME 114. In such examples, the SDK and/or media providersoftware may anonymize the data to protect the identity of the user 104.

The media device 106 of FIG. 1 is a device that retrieves media from themedia provider 102 for presentation or output. In some examples, themedia device 106 is capable of directly presenting media (e.g., via adisplay or internal speakers) and/or indirectly presenting media (e.g.,via a connected display and/or connected speakers). For example, themedia device 106 of the illustrated example is a mobile phone (e.g.,smart phone). Alternatively, the media device 106 may be a gamingconsole (e.g., Xbox®, Playstation® 3, etc.), a streaming media device(e.g., a Google Chromecast, an Apple TV) a smart media device (e.g., aniPad, a tablet, etc.), digital media players (e.g., a Roku® mediaplayer, a Slingbox®, etc.), a smart television, a STB, a computingdevice, etc. The example media device 106 may utilize an application toaccess media from the media provider 102 via the network 112. Forexample, the media device 106 may use a media provider application tostream media and/or output media on-demand provided by the mediaprovider 102. As further described below, the media device 106 of theuser 104 includes the AME application 108 and the wireless interface110.

The AME application 108 of FIG. 1 is a meter that obtains the censusdata (e.g., ID3 tags) based on digital media from the media provider 102accessed by the example media device 106. For example, when the examplemedia device 106 obtains media (e.g., including the ID3 tags) from theexample media provider 102 to be output by the media device 106, the AMEapplication 108 gathers the received ID3 tags and transmits the ID3 tagsto the AME 114 using the example wireless interface 110. In someexamples, the AME application 108 is an application that may beinstalled by a panelist. In some examples, the AME application 108 is anSDK that is included with a media provider application based on anagreement between the media provider 102 and the AME 114.

The example wireless interface 110 of FIG. 1 transmits and receives datavia wireless communications (e.g., a cellular communication, a Wi-Ficommunication, etc.) using the example network 112. For example, thewireless interface 110 may receive media from the example media provider102 to be presented/output by the media device 106 and/or an externalcomponent connected (e.g., via a wired or wireless connection) to themedia device 106. Additionally, the wireless interface 110 may transmitcensus data (e.g., obtained ID3 tags corresponding to digital mediaaccessed by the media device 106) to the example AME 114 via the network112.

The example network 112 of the illustrated example of FIG. 1 is anetwork like the Internet, a wireless mobile telecommunications network(e.g., 2G, 3G, LTE, etc.), and/or a cellular network. However, theexample network 112 may be implemented using any suitable wired and/orwireless network(s) including, for example, one or more data buses, oneor more Local Area Networks (LANs), one or more wireless LANs, one ormore cellular networks, one or more private networks, one or more publicnetworks, etc. The example network 112 enables the media provider 102,the media device 106, and the AME 114 to communicate data (e.g., media,media monitoring data, SDK census data, etc.) between each other.

The example AME 114 of FIG. 1 is a central facility that may beimplemented by a server that collects and processes census dataincluding ID3 tags from the media device 106 and other media devices ina region, location, and/or universe to monitor distribution of digitalmedia provided by the media provider 102 via the example network 112.The AME 114 analyzes the census information to determine whether thereis a transcoder outage, whether identified clients are dynamicallyinserting advertisement during national commercials, etc., for example.The census information may also be correlated or processed with factorssuch as geodemographic data (e.g., a geographic location of the mediaexposure measurement location, age(s) of the panelist(s) associated withthe media exposure measurement location, an income level of a panelist,etc.) Census information may be useful to manufacturers and/oradvertisers to validate agreement/contract terms with a media provider,identify transcoder outages, determine which features should beimproved, determine which features are popular among users, identifygeodemographic trends with respect to media presentation devices,identify market opportunities, and/or otherwise evaluate their ownand/or their competitors' products. The example AME 114 includes theexample media analyzer 116 and the example database 118, as furtherdescribed below.

The example media analyzer 116 of FIG. 1 processes received census datato obtain ID3 tags and decrypt the filtered ID3 tags to be able tovalidate agreements with the media provider 102. Because the clock/timerused to timestamp the ID3 tags may be off (e.g., fast or slow), theexample media analyzer 116 performs an alignment process to offset theinaccurate timestamps. Additionally, to reduce processor resources, themedia analyzer 116 filters out duplicate ID3 tags prior to decryptingthe ID3 tags. An example implementation of the example media analyzer116 is further described below in conjunction with FIG. 2.

In the illustrated example of FIG. 1, the AME 114 includes the exampledatabase 118 to store ID3 information and/or validation information. Thedatabase 118 may be implemented by a volatile memory (e.g., aSynchronous Dynamic Random Access Memory (SDRAM), Dynamic Random AccessMemory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM), etc.) and/ora non-volatile memory (e.g., flash memory). The database 118 mayadditionally or alternatively be implemented by one or more double datarate (DDR) memories, such as DDR, DDR2, DDR3, DDR4, mobile DDR (mDDR),etc. The database 118 may additionally or alternatively be implementedby one or more mass storage devices such as hard disk drive(s), compactdisk drive(s), digital versatile disk drive(s), solid-state diskdrive(s), etc. While in the illustrated example of FIG. 1 the database118 is illustrated as a single database, the database 118 may beimplemented by any number and/or type(s) of databases. Furthermore, thedata stored in the database 118 may be in any data format such as, forexample, binary data, comma delimited data, tab delimited data,structured query language (SQL) structures, etc.

FIG. 2 is a block diagram of an example implementation of the mediaanalyzer 116 of FIG. 1. The example media analyzer 116 of FIG. 2includes an example interface 200, an example tag processor 202, anexample filter 204, an example decrypter 206, an example orderer 208, anexample data shifter 210, an example comparator 212, and an examplevalidator 214.

The example interface 200 of FIG. 2 receives data (e.g., census data)from the example media device 106 and other media devices via thenetwork 112 of FIG. 1. The census data may correspond to a particularlocation and/or duration of time or may be pre-filtered by the AME 114to separate the census data into predefined regions and/or predefineddurations of time. Additionally, the interface 200 may transmit thecensus data and/or validation data to the example database 118 to bestored. In some examples, the interface 200 may transmit an alert when,for example, the example validator 214 flags the census data (e.g.,because it does not follow an agreement). In such examples, theinterface 200 may transmit the alert to an administrator, a client,and/or another processor of the AME 114. The alert may include a reportthat corresponds to the validation process.

The example tag processor 202 of FIG. 2 processes ID3 tags of the censusdata. For example, the tag processor 202 extracts raw ID3 tags from thecensus data. The raw census data packet may include ID3 data tags, InfoTags, IPs, Device identifiers, software developer kit (SDK) versions(e.g., corresponding to the version of the AME application 108), devicetype information, etc. To save resources, the tag processor 202 extractsonly the ID3 data tag from the census data. As further described below,the example filter 204 filters out duplicate census data based on theextracted ID3 data tag to reduce the amount of ID3 decryption that willoccur (e.g., which corresponds to the most resources to compute).Additionally, the tag processor 202 may process the ID3 tag informationand/or the metadata of the ID3 tags (e.g., after decryption) to assistthe filter 204, as further described below.

The example filter 204 of FIG. 2 filters out ID3 tags that appear lessthan a threshold number of times in the census data. ID3 tags thatappear less than a threshold number of times likely correspond to ID3extraction errors and/or ID3 generation errors. Accordingly, the examplefilter 204 filters out such ID3 tags to remove potential errors in thecensus data. The threshold number may be set to one and/or any othernumber based on user, client, and/or manufacturer preferences.Additionally, the example filter 204 may filter metadata stored in anID3 tag once the remaining ID3 tags are decrypted to obtain themetadata. For example, the tag processor 202 may identify mediaproviders (e.g., corresponding to stations, channels, etc.)corresponding to the obtained ID3 tags based on the metadata of the ID3tag. In such an example, the filter 204 may filter the ID3 tags and/orcorresponding metadata based on the identified stations, channels, etc.using the content identifier (CID) of the ID3 tags. Additionally, theexample filter 204 may filter out duplicate ID3 tags. For example, whentwo or more AME applications of two or more media devices access thesame media, the two or more media devices transmit the samecorresponding ID3 tags to the AME 114. Accordingly, there may bemultiple (e.g., hundreds, thousands, etc.) duplicate ID3 tags obtainedby the AME 114. Accordingly, before the decrypter 206 decrypts the ID3tags, the example filter 204 filters out duplicate ID3 tags based on theraw ID3 tag information, thereby reducing the number of ID3 tags thatneed to be decrypted without losing any information. In some examples,the filter 204 is multiple filters to perform the multiple filteringprocesses.

The example decrypter 206 of FIG. 2 decrypts the ID3 tags to identifythe metadata stored therein. Because the decryption process is resourceintensive, the example decrypter 206 decrypts the deduplicated ID3 tags(e.g., duplicates filtered out by the example filter 204), therebyconserving processor resources. The metadata included in the ID3 tagsmay include information corresponding to ancillary codes (e.g.,watermarks), timestamp information, CID information, offset information,etc. Accordingly, decrypting the ID3 tag allows the media analyzer 116to process the metadata for validation.

The example orderer 208 of FIG. 2 orders the metadata informationaccording to a start time encoded in the ID3 tag. As described above, atranscoder 102 of the media provider 102 transcodes AME watermarks inaudio by extracting the AME watermarks and bundling the AME watermarksinto an ID3 tag. The transcoder includes a start time corresponding towhen the transcoding began to process the audio for the ID3 tag, an endtime corresponding to when the transcoder stopped processing the audiofor the ID3 tag, and watermark offset times corresponding to when thetranscoder detected each watermark (e.g., the amount of time after thestart time). To determine the transcoding time of a code/watermark, theexample tag processor 202 may identify the first timestamp of the ID3tag and add the code/watermark offset, as described above in conjunctionwith FIG. 1. Alternatively, the tag processor 202 may determine thetranscoding time of a code/watermark based on a timestamp of thecode/watermark itself. However, not all codes/watermarks may betimestamped, but all codes/watermarks will include an offset. Once thetag processor 202 identifies/determines the transcoding time of thecodes/watermarks, the example orderer 208 orders the codes/watermarks(e.g., metadata) based on the transcoding time (e.g., by performing asimple sort).

Once the codes/watermarks are stored based on transcoding time andfiltered by station, the example data shifter 210 and the examplecomparator 212 of FIG. 2 align the station-based ordered metadata (e.g.,code/watermark data) with a station-based media monitoring system (MMS)linear reference data representative of accurate broadcast data. Asdescribed above, the timer/clock used by the example AME application 108may be inaccurate (e.g., slow or fast by X number of seconds, minutes,etc.). However, clocks/timers used by almost all modern electronicdevices are precise (e.g., regardless of how fast or slow theclock/timer is, the clock/timer's measurement of a second is precise).Additionally, the example AME 114 may require that the clock used by theAME application 108 be precise, but not necessarily accurate. Becausethe amount of time that the clock/timer is off by is unknown, theexample data shifter 210 applies different offsets to the timinginformation of the watermarked data to more closely align with linearreference data. For example, the example comparator 212 determines anerror corresponding to a difference between code events of thestation-based ordered metadata and event codes of the station-basedlinear reference data. For example, the comparator 212 may comparecommercial codes (CCs) of the ordered metadata to CCs of thestation-based linear reference data to determine an error (e.g., anamount of differentiation or average difference based on time). Thesmaller the error, the closer the offset ordered metadata matches thelinear reference data. The example data shifter 210 applies the offsetcorresponding to the smallest error to the station-based to thestation-based metadata.

The example validator 214 of FIG. 2 validates information (e.g., anagreement, a contract, etc.) based on the adjusted/aligned station-basedmetadata. For example, the agreement may include start and end times ofcommercial pods during a duration of time for a specific channel.Accordingly, the example validator 214 compares the CCs of theadjusted/aligned ordered metadata corresponding to the station to thecommercial pod duration to determine how many of the CCs are within oroutside of the designed commercial pods (e.g., potentially correspondingto additional advertisements that may have been inserted into media).Additionally or alternatively, the example validator 214 may determineif CCs are absent from the commercial pods (e.g., potentiallycorresponding to transcoder errors). In some examples the validator 214flags data based on some criteria (e.g., more than a threshold number orpercentage of CC codes outside of a commercial pod within a predefinedduration of time) and instructs the interface 200 to transmit a warningcorresponding to the flag.

While an example manner of implementing the example media analyzer 116of FIG. 1 is illustrated in FIG. 2, one or more of the elements,processes and/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example interface 200, the example tag processor 202, theexample filter 204, the example decrypter 206, the example orderer 208,the example data shifter 210, the example comparator 212, the examplevalidator 214, and/or, more generally the media analyzer 116 of FIG. 2may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example interface 200, the example tag processor 202, theexample filter 204, the example decrypter 206, the example orderer 208,the example data shifter 210, the example comparator 212, the examplevalidator 214, and/or, more generally the media analyzer 116 of FIG. 2could be implemented by one or more analog or digital circuit(s), logiccircuits, programmable processor(s), programmable controller(s),graphics processing unit(s) (GPU(s)), digital signal processor(s)(DSP(s)), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)). When reading any of the apparatus or system claimsof this patent to cover a purely software and/or firmwareimplementation, at least one of the example interface 200, the exampletag processor 202, the example filter 204, the example decrypter 206,the example orderer 208, the example data shifter 210, the examplecomparator 212, the example validator 214, and/or, more generally themedia analyzer 116 of FIG. 2 is and/or are hereby expressly defined toinclude a non-transitory computer readable storage device or storagedisk such as a memory, a digital versatile disk (DVD), a compact disk(CD), a Blu-ray disk, etc. including the software and/or firmware.Further still, the example media analyzer 116 of FIG. 2 may include oneor more elements, processes and/or devices in addition to, or insteadof, those illustrated in FIG. 2, and/or may include more than one of anyor all of the illustrated elements, processes and devices. As usedherein, the phrase “in communication,” including variations thereof,encompasses direct communication and/or indirect communication throughone or more intermediary components, and does not require directphysical (e.g., wired) communication and/or constant communication, butrather additionally includes selective communication at periodicintervals, scheduled intervals, aperiodic intervals, and/or one-timeevents.

Flowcharts representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the example media analyzer 116 ofFIG. 2 are shown in FIGS. 3-4. The machine readable instructions may bean executable program or portion of an executable program for executionby a computer processor such as the processor 512 shown in the exampleprocessor platform 500 discussed below in connection with FIG. 5. Theprogram may be embodied in software stored on a non-transitory computerreadable storage medium such as a CD-ROM, a floppy disk, a hard drive, aDVD, a Blu-ray disk, or a memory associated with the processor 512, butthe entire program and/or parts thereof could alternatively be executedby a device other than the processor 512 and/or embodied in firmware ordedicated hardware. Further, although the example program is describedwith reference to the flowcharts illustrated in FIGS. 3-4, many othermethods of implementing the example media analyzer 116 of FIG. 2 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined. Additionally or alternatively, any or all ofthe blocks may be implemented by one or more hardware circuits (e.g.,discrete and/or integrated analog and/or digital circuitry, an FPGA, anASIC, a comparator, an operational-amplifier (op-amp), a logic circuit,etc.) structured to perform the corresponding operation withoutexecuting software or firmware.

As mentioned above, the example process of FIGS. 3-4 may be implementedusing executable instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C.

FIG. 3 is an example flowchart 300 representative of example machinereadable instructions that may be executed by the example media analyzer116 of FIGS. 1 and/or 2 to monitor and/or validate digital media.Although the flowchart 300 of FIG. 3 is described in conjunction withmedia analyzer 116 of FIGS. 1 and/or 2, other type(s) of mediaanalyzer(s), and/or other type(s) of processor(s) may be utilizedinstead.

At block 302, the example interface 200 obtains census data from theexample client device 106 and other client devices of other panelists.As described above, the census data includes ID3 tags corresponding tomedia transmitted to and displayed by the example client device 106.Each ID3 tag may include metadata corresponding to watermarks of themedia. At block 304, the example tag processor 202 extracts raw ID3 tagfrom census data. Duplicate ID3 tags will be the same. Thus, the examplemedia analyzer 116 can identify duplicate ID3 tags from the extractedraw ID3 tags prior to decryption.

At block 306, the example tag processor 202 determines if the number ofeach unique ID3 tag meets a threshold. As described above in, when anID3 is inserted into media, the census data will likely include hundredsor more of the ID3 tags from the multiple meters in the universe.Accordingly, if less than a threshold number of a specific ID3 tag isincluded in the census data (e.g., 1 or 2), then it is probable that theID3 tag is an error (e.g., and can be discarded). If the example tagprocessor 202 determines that a total number of one or more ID3 tagsdoes not the (block 306: NO), the example filter 204 discards (e.g.,filters out) the ID3 tags that do not meet the threshold (block 308).

At block 310, the example filter 204 discards (e.g., by filtering out)duplicate ID3 tags. Because there are likely to be hundreds or moreduplicate ID3 tags, decrypting the hundreds of duplicate ID3 tagsrequires lots of processor resources without providing any additionaldata. Accordingly, the example filter 204 discards the duplicate ID3tags to conserve processor resources. At block 312, the exampledecrypter 206 decrypts the remaining ID3 tags to identify the storedmetadata (e.g., corresponding to watermarks). At block 314, the exampleorderer 208 orders the metadata based on timing data of the metadata.For example, the tag processor 202 may determine the transcoding time ofthe individual watermarks of the ID3 tags by adding the offset of theindividual codes/watermarks to the start time of the ID3 tag. Once thetag processor 202 determines the transcoding time of thecodes/watermarks in the metadata, the example orderer 208 orders thecodes/watermarks based on the transcoding time of the codes/watermarks.

At block 316, the example filter 204 separates the metadata according tostation to generate station-based ordered metadata. For example, the tagprocessor 202 may identify the station identifier (SID) of theindividual codes/watermarks and the example filter 204 may separate thecodes/watermarks according to the SIDs. For each station-based orderedmetadata (blocks 318-324), the example media analyzer 116 generates anadjusted ordered metadata by aligning code events of the orderedmetadata (block 320). At block 322, the example validator 214 validatescriteria based on the adjusted/aligned ordered metadata. For example, asdescribed above in conjunction with FIG. 2, the validator 214 mayidentify whether an agreement between the media provider 102 and the AME114 is satisfied by determining whether CC codes of the adjusted orderedmetadata fall within predetermined commercial pods by comparing theadjusted ordered metadata with the predefined commercial podinformation. At block 326, the example interface 200 stores the examplevalidation data/results in the example database 118 of FIG. 1. In someexamples, the interface 200 may also transmit an alert to a user,customer, client, and/or system administrator in response to avalidation. The alert may include a report corresponding to thevalidation process.

FIG. 4 is an example flowchart 320 representative of example machinereadable instructions that may be executed by the example media analyzer116 of FIGS. 1 and/or 2 to generate adjusted ordered metadata byaligning code events, as described above in conjunction with block 320of FIG. 3. Although the flowchart 320 of FIG. 4 is described inconjunction with media analyzer 116 of FIGS. 1 and/or 2, other type(s)of media analyzer(s), and/or other type(s) of processor(s) may beutilized instead.

At block 402, the example data shifter 210 selects a first offset from aset of offsets. The number and/or interval of the offsets may be basedon an amount of alignment accuracy that is desired by a user and/orclient. For example, the offsets may correspond to any number of secondintervals, minute intervals, millisecond intervals, etc. At block 404,the example data shifter 210 adjusts (e.g., shifts) the ordered metadatausing the selected offset. For example, if the offset is +1 second, theexample data shifter 210 shifts each ordered watermark/codes in theordered metadata by +1 second.

At block 406, the example comparator 212 compares the adjusted orderedmetadata to reference linear data. For example, the comparator 212 maycompare the CC codes of the example adjusted ordered metadata to the CCsof the reference linear data to see if the transducing times of the CCsof the adjusted ordered metadata match the timestamps of the CCs of thereferenced linear data. At block 408, the example comparator 212computes an error based on the comparison. For example, the comparator212 may determine a difference (e.g., an average difference) betweentranscoding times of CCs of the adjusted ordered metadata to thetranscoding times of CCs of the reference linear data. At block 410, theexample comparator 212 stores the computed error in conjunction with theselected offset in local memory (e.g., the example local memory 513 ofFIG. 5). At block 412, the example data shifter 210 determines if thereare additional offsets from the set of offsets.

If the example data shifter 210 determines that there are additionaloffsets from the set of offsets (block 412: YES), the data shifter 210selects a subsequent offset from the set of offsets (block 414), and theprocess returns to block 404. If the example data shifter 210 determinesthat there are no additional offsets from the set of offsets (block 412:NO), the example data shifter 210 selects the offset corresponding tothe lowest computed error from the local memory (e.g., the example localmemory 513) (block 416). At block 418, the example data shifter 210adjusts the ordered metadata using the selected offset to generate theadjusted ordered metadata. For example, if the offset is +1 second, theexample data shifter 210 shifts each ordered code/watermark in theordered metadata by +1 second.

FIG. 5 is a block diagram of an example processor platform 500structured to execute the instructions of FIG. 3-4 to implement theexample media analyzer 116 of FIG. 2. The processor platform 500 can be,for example, a server, a personal computer, a workstation, aself-learning machine (e.g., a neural network), a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad®), or any other typeof computing device.

The processor platform 500 of the illustrated example includes aprocessor 512. The processor 512 of the illustrated example is hardware.For example, the processor 512 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors, GPUs, DSPs, orcontrollers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor 512 implements the example interface 200,the example tag processor 202, the example filter 204, the exampledecrypter 206, the example orderer 208, the example data shifter 210,the example comparator 212, and the example validator 214 of FIG. 2.

The processor 512 of the illustrated example includes a local memory 513(e.g., a cache). The processor 512 of the illustrated example is incommunication with a main memory including a volatile memory 514 and anon-volatile memory 516 via a bus 518. The volatile memory 514 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory(RDRAM®) and/or any other type of random access memory device. Thenon-volatile memory 516 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 514, 516is controlled by a memory controller.

The processor platform 500 of the illustrated example also includes aninterface circuit 520. The interface circuit 520 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 522 are connectedto the interface circuit 520. The input device(s) 522 permit(s) a userto enter data and/or commands into the processor 512. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, and/or a voice recognitionsystem.

One or more output devices 524 are also connected to the interfacecircuit 520 of the illustrated example. The output devices 524 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 520 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 520 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 526. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 500 of the illustrated example also includes oneor more mass storage devices 528 for storing software and/or data.Examples of such mass storage devices 528 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 532 of FIGS. 3-4 may be stored inthe mass storage device 528, in the volatile memory 514, in thenon-volatile memory 516, and/or on a removable non-transitory computerreadable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been to monitor digitalmedia. The disclosed methods, apparatus and articles of manufacture fixthe technical problem of obtaining ID3 tags including inaccurate timinginformation from inaccurate clocks by aligning codes of obtainedmetadata with reference data to provide more accurate validationprocesses. Additionally, examples disclosed herein reduce the amount ofprocessor resources needed to process ID3 tags to perform validationprocesses by deduplicating ID3 tags prior to decrypting the ID3 tags.Disclosed methods, apparatus and articles of manufacture are accordinglydirected to one or more technical improvement(s) to a processor used tomonitor and process ID3 tags.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus to increase an accuracy ofinaccurate timing information of metadata, the apparatus comprising:means for generating a plurality of errors by comparing (a) a pluralityof ordered metadata adjusted using a plurality of offsets and (b)reference data, the adjusted ordered metadata corresponding to metadatawith inaccurate timing information for media; and means for adjustingtiming information to: identify an offset of the plurality of offsetsbased on the plurality of errors; and adjust the timing information ofthe metadata using the offset to increase an accuracy of the timinginformation.
 2. The apparatus of claim 1, wherein the metadata isincluded in an ID3 tag encoded in the media.
 3. The apparatus of claim2, wherein the ID3 tag includes the inaccurate timing information due toan inaccurate clock of a media device.
 4. The apparatus of claim 2,wherein the timing information corresponds to a sum of a transcodingtime of the ID3 tag and a second offset of the metadata.
 5. Theapparatus of claim 1, wherein the metadata corresponds to ancillarycodes.
 6. The apparatus of claim 1, wherein the means for generating isto calculate an error of the plurality of errors by determining adifference between a first code in the adjusted ordered metadata and asecond code in the reference data.
 7. The apparatus of claim 1, furtherincluding means for performing a validation process based on theadjusted ordered metadata and a commercial pod corresponding to anagreement.
 8. An apparatus to increase an accuracy of inaccurate timinginformation of metadata, the apparatus comprising: memory; instructionsin the apparatus; and a processor to execution the instructions to:generate a plurality of errors by comparing (a) a plurality of orderedmetadata adjusted using a plurality of offsets and (b) reference data,the adjusted ordered metadata corresponding to metadata with inaccuratetiming information for media; identify an offset of the plurality ofoffsets based on the plurality of errors; and adjust timing informationof the metadata using the offset to increase an accuracy of the timinginformation.
 9. The apparatus of claim 8, wherein the metadata isincluded in an ID3 tag encoded in the media.
 10. The apparatus of claim9, wherein the ID3 tag includes the inaccurate timing information due toan inaccurate clock of a media device.
 11. The apparatus of claim 9,wherein the timing information corresponds to a sum of a transcodingtime of the ID3 tag and a second offset of the metadata.
 12. Theapparatus of claim 8, wherein the metadata corresponds to ancillarycodes.
 13. The apparatus of claim 8, wherein the processor is tocalculate an error of the plurality of errors by determining adifference between a first code in the adjusted ordered metadata and asecond code in the reference data.
 14. The apparatus of claim 8, whereinthe processor is to perform a validation process based on the adjustedordered metadata and a commercial pod corresponding to an agreement. 15.A non-transitory computer readable storage medium comprisinginstructions which, when executed, cause one or more processors to atleast: generate a plurality of errors by comparing (a) a plurality ofordered metadata adjusted using a plurality of offsets and (b) referencedata, the adjusted ordered metadata for metadata with inaccurate timinginformation corresponding to media; identify an offset of the pluralityof offsets based on the plurality of errors; and adjust timinginformation of the metadata using the offset to increase an accuracy ofthe timing information.
 16. The non-transitory computer readable storagemedium of claim 15, wherein the metadata is included in an ID3 tagencoded in the media.
 17. The non-transitory computer readable storagemedium of claim 16, wherein the ID3 tag includes the inaccurate timinginformation due to an inaccurate clock of a media device.
 18. Thenon-transitory computer readable storage medium of claim 16, wherein thetiming information corresponds to a sum of a transcoding time of the ID3tag and a second offset of the metadata.
 19. The non-transitory computerreadable storage medium of claim 15, wherein the metadata corresponds toancillary codes.
 20. The non-transitory computer readable storage mediumof claim 15, wherein the instructions cause the one or more processorsto calculate an error of the plurality of errors by determining adifference between a first code in the adjusted ordered metadata and asecond code in the reference data.